code: def test_lambda_audio() -> None:
      opts = get_config()
  
      input = {
          "samples": {
              "audio": torch.rand(
                  2, 5, 1, 7
              ),  # arbitrary number of dimensions of arbitrary lengths
              "xyz": torch.rand(3, 9),  # arbitrary key, value
          },
          "targets": {
              "xyz2": torch.rand(4, 8),  # arbitrary key, value
          },
      }
  
      func = lambda x: (x + 1) * 2
      output = LambdaAudio(opts, func)(copy.deepcopy(input))
  
      nested_key_names = lambda dict_: {
          key: value.keys() for key, value in dict_.items()
      }
      assert nested_key_names(input) == nested_key_names(output)
  
      assert torch.allclose(
          output["samples"]["audio"], func(input["samples"]["audio"])
      )
      assert torch.allclose(
          output["samples"]["xyz"], input["samples"]["xyz"]
      )
      assert torch.allclose(
          output["targets"]["xyz2"], input["targets"]["xyz2"]
      )
  
file_path: ml-cvnets/tests/transforms/test_audio.py
imports: import copy
  import os
  import shutil
  import tempfile
  import pytest
  import scipy.io.wavfile
  import torch
  from data.transforms.audio import AudioResample, Gain, LambdaAudio, MFCCs, Noise, Roll, SetFixedLength, StandardizeChannels
  from tests.configs import get_config
label_desc: Use image node_id 2 for calling a global function with example usage: test_lambda_audio() without return types